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NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity

Fig 2

Probability maps for tensor-based microstructural indices, respectively.

These probability maps are represented as 68 × 68 square arrays with a value in each cell that represents the probability of each structural connection to be selected during randomised Lasso bootstrap iterations. NSTREAMS represent that the underlying structural connectivity matrices were derived based on the number of streamlines divided by the average number of voxels within the two end-point ROIs. WFA and WMD reflect that the underlying structural connectivity matrices are derived as a weighted average of the fractional anisotropy (FA) and the mean diffusivity (MD) along the streamlines, respectively. On the other hand, δ, θ, α, β and γ represent that the underlying EEG functional connectivity matrices have been derived based on bandpass filtering in five frequency bands 1–4Hz, 4–8Hz, 8–13Hz, 13–30Hz and 30–70Hz, respectively.

Fig 2